Events on Tuesday, April 17th, 2012

Abstract: Computational complexity has been the subject of intensive research with rewarding theoretical and practical accomplishments. ShannonaEuroTMs mathematical formalism to quantify and study digitized information has provided a powerful framework for applications to science and engineering. In biology, the analog nature of the system observables and aEurooesignal-encodingaEuro pose a formidable challenge to draw biologically insightful parallels between the digital information theory and the analog theory of biological information, even if such a theory could potentially be developed.<br>
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We provide an outline of a systematic program that aims to better understand the phenomena generally recognized as the culprit to aEurooebiological complexityaEuro, namely, variation of phenotypic traits within a single genotype. A quantitative theory of phenotypic variation leads to the theory of biological complexity at DNA level; which is as a central question in theoretical biology, and sheds new light on the evolution of diversity of life.<br>
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We use the physics approach to extract the hints from a data set of gene expression time-series (courtesy of the Chory Lab, Salk Institute), regarded as observations from a complex dynamical system in the ground state and subject to various perturbations. We provide a sketch of the steps to compute variations at the very first molecular stage past the DNA. In parallel to KolmogrovaEuroTMs theory of computational complexity, we propose a multi-scale multi-resolution theory to elucidate ideal measures of the most efficient description of computations initiated at the genome level, leading to the phenotypic observables. We discuss an application to aEurooequantifying phenotypic plasticityaEuro and formulate new hypotheses regarding the DNA-level sources and molecular mechanisms of plasticity. These results are obtained via massively parallel and distributed computation, which offer exciting new research problems of their own. <br>